





"""simple docstring"""


import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__       :					Tuple						=			data_utils.TransfoXLTokenizer
__magic_name__       :					Any						=			data_utils.TransfoXLCorpus
__magic_name__       :					Optional[int]						=			data_utils
__magic_name__       :					str						=			data_utils

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						if transfo_xl_dataset_file:
												# Convert a pre-processed corpus (see original TensorFlow repo)
												with open(_lowerCamelCase							,     """rb"""     ) as fp:
																		UpperCamelCase				:		Dict				   =			pickle.load(_lowerCamelCase							,     encoding="""latin1"""     )
												# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
												UpperCamelCase				:		List[str]				   =			pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""pretrained_vocab_file"""]
												print(f"""Save vocabulary to {pytorch_vocab_dump_path}"""     )
												UpperCamelCase				:		Any				   =			corpus.vocab.__dict__
												torch.save(_lowerCamelCase							,     _lowerCamelCase     )

												UpperCamelCase				:		Tuple				   =			corpus.__dict__
												corpus_dict_no_vocab.pop("""vocab"""							,     _lowerCamelCase     )
												UpperCamelCase				:		List[Any]				   =			pytorch_dump_folder_path + """/""" + CORPUS_NAME
												print(f"""Save dataset to {pytorch_dataset_dump_path}"""     )
												torch.save(_lowerCamelCase							,     _lowerCamelCase     )

						if tf_checkpoint_path:
												# Convert a pre-trained TensorFlow model
												UpperCamelCase				:		Any				   =			os.path.abspath(_lowerCamelCase     )
												UpperCamelCase				:		Optional[Any]				   =			os.path.abspath(_lowerCamelCase     )

												print(f"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""     )
												# Initialise PyTorch model
												if transfo_xl_config_file == "":
																		UpperCamelCase				:		Tuple				   =			TransfoXLConfig()
												else:
																		UpperCamelCase				:		Optional[Any]				   =			TransfoXLConfig.from_json_file(_lowerCamelCase     )
												print(f"""Building PyTorch model from configuration: {config}"""     )
												UpperCamelCase				:		Dict				   =			TransfoXLLMHeadModel(_lowerCamelCase     )

												UpperCamelCase				:		Tuple				   =			load_tf_weights_in_transfo_xl(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase     )
												# Save pytorch-model
												UpperCamelCase				:		List[Any]				   =			os.path.join(_lowerCamelCase							,     _lowerCamelCase     )
												UpperCamelCase				:		Optional[Any]				   =			os.path.join(_lowerCamelCase							,     _lowerCamelCase     )
												print(f"""Save PyTorch model to {os.path.abspath(_lowerCamelCase     )}"""     )
												torch.save(model.state_dict()							,     _lowerCamelCase     )
												print(f"""Save configuration file to {os.path.abspath(_lowerCamelCase     )}"""     )
												with open(_lowerCamelCase							,     """w"""							,     encoding="""utf-8"""     ) as f:
																		f.write(config.to_json_string()     )


if __name__ == "__main__":
		__magic_name__       :					Tuple						=			argparse.ArgumentParser()
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default=None,
		    type=str,
		    required=True,
		    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""",
		    default="""""",
		    type=str,
		    help="""An optional path to a TensorFlow checkpoint path to be converted.""",
		)
		parser.add_argument(
		    """--transfo_xl_config_file""",
		    default="""""",
		    type=str,
		    help=(
		        """An optional config json file corresponding to the pre-trained BERT model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--transfo_xl_dataset_file""",
		    default="""""",
		    type=str,
		    help="""An optional dataset file to be converted in a vocabulary.""",
		)
		__magic_name__       :					Optional[int]						=			parser.parse_args()
		convert_transfo_xl_checkpoint_to_pytorch(
		    args.tf_checkpoint_path,
		    args.transfo_xl_config_file,
		    args.pytorch_dump_folder_path,
		    args.transfo_xl_dataset_file,
		)











"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						int = 10						,							_lowerCamelCase			:						int = 1000						,							_lowerCamelCase			:						bool = True					) ->							int:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
      and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), "Invalid type of value(s) specified to function!"

  if min_val > max_val:
    raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'					)
  return min_val if option else max_val







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							int:
  return int((number_a + number_a) / 2					)







def 	lowerCamelCase__			(				_lowerCamelCase			:						int						,							_lowerCamelCase			:						int						,							_lowerCamelCase			:						int					) ->							None:
  assert (
      isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					) and isinstance(_lowerCamelCase						,							_lowerCamelCase					)
  ), 'argument values must be type of "int"'

  if lower > higher:
    raise ValueError('argument value for lower and higher must be(lower > higher)'					)

  if not lower < to_guess < higher:
    raise ValueError(
        'guess value must be within the range of lower and higher value'					)

  def answer(_lowerCamelCase			:						int					) -> str:
    if number > to_guess:
      return "high"
    elif number < to_guess:
      return "low"
    else:
      return "same"

  print('started...'					)

  lowerCamelCase_       	=		lower
  lowerCamelCase_       	=		higher

  lowerCamelCase_       	=		[]

  while True:
    lowerCamelCase_       	=		get_avg(_lowerCamelCase						,							_lowerCamelCase					)
    last_numbers.append(_lowerCamelCase					)

    if answer(_lowerCamelCase					) == "low":
      lowerCamelCase_       	=		number
    elif answer(_lowerCamelCase					) == "high":
      lowerCamelCase_       	=		number
    else:
      break

  print(F'''guess the number : {last_numbers[-1]}'''					)
  print(F'''details : {last_numbers!s}'''					)







def 	lowerCamelCase__			(				) ->							None:
  lowerCamelCase_       	=		int(input('Enter lower value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter high value : '					).strip()					)
  lowerCamelCase_       	=		int(input('Enter value to guess : '					).strip()					)
  guess_the_number(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase					)


if __name__ == "__main__":
  main()




